Overview

Dataset statistics

Number of variables28
Number of observations99112
Missing cells16251
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.9 MiB
Average record size in memory253.3 B

Variable types

Categorical13
DateTime2
Numeric13

Alerts

customer_id has a high cardinality: 99112 distinct valuesHigh cardinality
order_approved_at has a high cardinality: 90411 distinct valuesHigh cardinality
order_delivered_carrier_date has a high cardinality: 80749 distinct valuesHigh cardinality
order_delivered_customer_date has a high cardinality: 95394 distinct valuesHigh cardinality
order_estimated_delivery_date has a high cardinality: 423 distinct valuesHigh cardinality
product_most_frequent has a high cardinality: 31695 distinct valuesHigh cardinality
customer_unique_id has a high cardinality: 95780 distinct valuesHigh cardinality
customer_city has a high cardinality: 4116 distinct valuesHigh cardinality
product_category_name_english has a high cardinality: 72 distinct valuesHigh cardinality
payment_value is highly overall correlated with sum_price and 2 other fieldsHigh correlation
sum_price is highly overall correlated with payment_value and 1 other fieldsHigh correlation
sum_freight_value is highly overall correlated with payment_valueHigh correlation
product_weight_g is highly overall correlated with payment_value and 4 other fieldsHigh correlation
product_length_cm is highly overall correlated with product_weight_g and 1 other fieldsHigh correlation
product_height_cm is highly overall correlated with product_weight_gHigh correlation
product_width_cm is highly overall correlated with product_weight_g and 1 other fieldsHigh correlation
order_status is highly imbalanced (91.5%)Imbalance
payment_type is highly imbalanced (61.1%)Imbalance
order_delivered_carrier_date has 1735 (1.8%) missing valuesMissing
order_delivered_customer_date has 2908 (2.9%) missing valuesMissing
customer_id is uniformly distributedUniform
order_approved_at is uniformly distributedUniform
order_delivered_carrier_date is uniformly distributedUniform
order_delivered_customer_date is uniformly distributedUniform
customer_unique_id is uniformly distributedUniform
customer_id has unique valuesUnique
length_comment_title has 86798 (87.6%) zerosZeros
length_comment_message has 57807 (58.3%) zerosZeros
product_description_lenght has 1418 (1.4%) zerosZeros
product_photos_qty has 1418 (1.4%) zerosZeros

Reproduction

Analysis started2023-02-10 09:55:27.080754
Analysis finished2023-02-10 09:56:10.432704
Duration43.35 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

customer_id
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct99112
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
9ef432eb6251297304e76186b10a928d
 
1
30fe36e40e801f6f55cce8ee4aae9da3
 
1
a4fe94a051d268fbbe8e4ca932ebc460
 
1
ba712872211b52224c61d5bedfc1bfcf
 
1
f8b67d327058afa39382991d7173b1d7
 
1
Other values (99107)
99107 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3171584
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique99112 ?
Unique (%)100.0%

Sample

1st row9ef432eb6251297304e76186b10a928d
2nd rowb0830fb4747a6c6d20dea0b8c802d7ef
3rd row41ce2a54c0b03bf3443c3d931a367089
4th rowf88197465ea7920adcdbec7375364d82
5th row8ab97904e6daea8866dbdbc4fb7aad2c

Common Values

ValueCountFrequency (%)
9ef432eb6251297304e76186b10a928d 1
 
< 0.1%
30fe36e40e801f6f55cce8ee4aae9da3 1
 
< 0.1%
a4fe94a051d268fbbe8e4ca932ebc460 1
 
< 0.1%
ba712872211b52224c61d5bedfc1bfcf 1
 
< 0.1%
f8b67d327058afa39382991d7173b1d7 1
 
< 0.1%
110b79f06a0f49a38da99084706a382d 1
 
< 0.1%
366b4b63cda57be7ca46ecb33ee71f4e 1
 
< 0.1%
52798469029a20d7814d2b58c0c63e0d 1
 
< 0.1%
4ddbeaafc3eff2a014e49052df6c530f 1
 
< 0.1%
ab994eee6b515cbcf023c206bf29ec08 1
 
< 0.1%
Other values (99102) 99102
> 99.9%

Length

2023-02-10T10:56:10.500140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9ef432eb6251297304e76186b10a928d 1
 
< 0.1%
8b212b9525f9e74e85e37ed6df37693e 1
 
< 0.1%
503740e9ca751ccdda7ba28e9ab8f608 1
 
< 0.1%
ed0271e0b7da060a393796590e7b737a 1
 
< 0.1%
9bdf08b4b3b52b5526ff42d37d47f222 1
 
< 0.1%
f54a9f0e6b351c431402b8461ea51999 1
 
< 0.1%
31ad1d1b63eb9962463f764d4e6e0c9d 1
 
< 0.1%
494dded5b201313c64ed7f100595b95c 1
 
< 0.1%
a166da34890074091a942054b36e4265 1
 
< 0.1%
7711cf624183d843aafe81855097bc37 1
 
< 0.1%
Other values (99102) 99102
> 99.9%

Most occurring characters

ValueCountFrequency (%)
5 198650
 
6.3%
2 198612
 
6.3%
f 198600
 
6.3%
c 198574
 
6.3%
1 198471
 
6.3%
8 198465
 
6.3%
b 198460
 
6.3%
3 198401
 
6.3%
7 198265
 
6.3%
e 198086
 
6.2%
Other values (6) 1187000
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1981935
62.5%
Lowercase Letter 1189649
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 198650
10.0%
2 198612
10.0%
1 198471
10.0%
8 198465
10.0%
3 198401
10.0%
7 198265
10.0%
6 198083
10.0%
9 198032
10.0%
0 197652
10.0%
4 197304
10.0%
Lowercase Letter
ValueCountFrequency (%)
f 198600
16.7%
c 198574
16.7%
b 198460
16.7%
e 198086
16.7%
a 198006
16.6%
d 197923
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1981935
62.5%
Latin 1189649
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
5 198650
10.0%
2 198612
10.0%
1 198471
10.0%
8 198465
10.0%
3 198401
10.0%
7 198265
10.0%
6 198083
10.0%
9 198032
10.0%
0 197652
10.0%
4 197304
10.0%
Latin
ValueCountFrequency (%)
f 198600
16.7%
c 198574
16.7%
b 198460
16.7%
e 198086
16.7%
a 198006
16.6%
d 197923
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3171584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 198650
 
6.3%
2 198612
 
6.3%
f 198600
 
6.3%
c 198574
 
6.3%
1 198471
 
6.3%
8 198465
 
6.3%
b 198460
 
6.3%
3 198401
 
6.3%
7 198265
 
6.3%
e 198086
 
6.2%
Other values (6) 1187000
37.4%

order_status
Categorical

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
delivered
96211 
shipped
 
1098
unavailable
 
602
canceled
 
599
processing
 
299
Other values (3)
 
303

Length

Max length11
Median length9
Mean length8.9838566
Min length7

Characters and Unicode

Total characters890408
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdelivered
2nd rowdelivered
3rd rowdelivered
4th rowdelivered
5th rowdelivered

Common Values

ValueCountFrequency (%)
delivered 96211
97.1%
shipped 1098
 
1.1%
unavailable 602
 
0.6%
canceled 599
 
0.6%
processing 299
 
0.3%
invoiced 296
 
0.3%
created 5
 
< 0.1%
approved 2
 
< 0.1%

Length

2023-02-10T10:56:10.626647image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-10T10:56:10.786187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
delivered 96211
97.1%
shipped 1098
 
1.1%
unavailable 602
 
0.6%
canceled 599
 
0.6%
processing 299
 
0.3%
invoiced 296
 
0.3%
created 5
 
< 0.1%
approved 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 292138
32.8%
d 194422
21.8%
i 98802
 
11.1%
l 98014
 
11.0%
v 97111
 
10.9%
r 96517
 
10.8%
p 2499
 
0.3%
a 2412
 
0.3%
c 1798
 
0.2%
n 1796
 
0.2%
Other values (7) 4899
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 890408
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 292138
32.8%
d 194422
21.8%
i 98802
 
11.1%
l 98014
 
11.0%
v 97111
 
10.9%
r 96517
 
10.8%
p 2499
 
0.3%
a 2412
 
0.3%
c 1798
 
0.2%
n 1796
 
0.2%
Other values (7) 4899
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 890408
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 292138
32.8%
d 194422
21.8%
i 98802
 
11.1%
l 98014
 
11.0%
v 97111
 
10.9%
r 96517
 
10.8%
p 2499
 
0.3%
a 2412
 
0.3%
c 1798
 
0.2%
n 1796
 
0.2%
Other values (7) 4899
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 890408
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 292138
32.8%
d 194422
21.8%
i 98802
 
11.1%
l 98014
 
11.0%
v 97111
 
10.9%
r 96517
 
10.8%
p 2499
 
0.3%
a 2412
 
0.3%
c 1798
 
0.2%
n 1796
 
0.2%
Other values (7) 4899
 
0.6%
Distinct98546
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Minimum2017-01-05 11:56:06
Maximum2018-10-17 17:30:18
2023-02-10T10:56:10.933630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:11.081463image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

order_approved_at
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct90411
Distinct (%)91.4%
Missing154
Missing (%)0.2%
Memory size1.5 MiB
2018-02-27 04:31:10
 
9
2018-02-27 04:31:01
 
7
2018-07-05 16:33:01
 
7
2018-02-06 05:31:52
 
7
2018-01-10 10:32:03
 
7
Other values (90406)
98921 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1880202
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique83367 ?
Unique (%)84.2%

Sample

1st row2017-10-02 11:07:15
2nd row2018-07-26 03:24:27
3rd row2018-08-08 08:55:23
4th row2017-11-18 19:45:59
5th row2018-02-13 22:20:29

Common Values

ValueCountFrequency (%)
2018-02-27 04:31:10 9
 
< 0.1%
2018-02-27 04:31:01 7
 
< 0.1%
2018-07-05 16:33:01 7
 
< 0.1%
2018-02-06 05:31:52 7
 
< 0.1%
2018-01-10 10:32:03 7
 
< 0.1%
2017-12-05 10:30:42 7
 
< 0.1%
2017-11-07 07:30:38 7
 
< 0.1%
2017-11-07 07:30:29 7
 
< 0.1%
2017-11-07 07:30:48 6
 
< 0.1%
2018-07-23 11:31:25 6
 
< 0.1%
Other values (90401) 98888
99.8%
(Missing) 154
 
0.2%

Length

2023-02-10T10:56:11.222029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-04-24 990
 
0.5%
2017-11-24 799
 
0.4%
2017-11-25 754
 
0.4%
2018-07-05 697
 
0.4%
2017-11-28 506
 
0.3%
2018-08-07 444
 
0.2%
2017-12-05 426
 
0.2%
2018-08-20 426
 
0.2%
2018-05-08 426
 
0.2%
2018-01-22 408
 
0.2%
Other values (42180) 192040
97.0%

Most occurring characters

ValueCountFrequency (%)
0 317386
16.9%
1 304121
16.2%
2 240526
12.8%
- 197916
10.5%
: 197916
10.5%
98958
 
5.3%
8 98198
 
5.2%
5 95335
 
5.1%
3 92887
 
4.9%
7 87855
 
4.7%
Other values (3) 149104
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1385412
73.7%
Dash Punctuation 197916
 
10.5%
Other Punctuation 197916
 
10.5%
Space Separator 98958
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 317386
22.9%
1 304121
22.0%
2 240526
17.4%
8 98198
 
7.1%
5 95335
 
6.9%
3 92887
 
6.7%
7 87855
 
6.3%
4 68666
 
5.0%
6 42186
 
3.0%
9 38252
 
2.8%
Dash Punctuation
ValueCountFrequency (%)
- 197916
100.0%
Other Punctuation
ValueCountFrequency (%)
: 197916
100.0%
Space Separator
ValueCountFrequency (%)
98958
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1880202
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 317386
16.9%
1 304121
16.2%
2 240526
12.8%
- 197916
10.5%
: 197916
10.5%
98958
 
5.3%
8 98198
 
5.2%
5 95335
 
5.1%
3 92887
 
4.9%
7 87855
 
4.7%
Other values (3) 149104
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1880202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 317386
16.9%
1 304121
16.2%
2 240526
12.8%
- 197916
10.5%
: 197916
10.5%
98958
 
5.3%
8 98198
 
5.2%
5 95335
 
5.1%
3 92887
 
4.9%
7 87855
 
4.7%
Other values (3) 149104
7.9%

order_delivered_carrier_date
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct80749
Distinct (%)82.9%
Missing1735
Missing (%)1.8%
Memory size1.5 MiB
2018-05-09 15:48:00
 
47
2018-05-10 18:29:00
 
32
2018-05-07 12:31:00
 
21
2018-05-02 15:15:00
 
16
2018-07-24 16:07:00
 
16
Other values (80744)
97245 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1850163
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70660 ?
Unique (%)72.6%

Sample

1st row2017-10-04 19:55:00
2nd row2018-07-26 14:31:00
3rd row2018-08-08 13:50:00
4th row2017-11-22 13:39:59
5th row2018-02-14 19:46:34

Common Values

ValueCountFrequency (%)
2018-05-09 15:48:00 47
 
< 0.1%
2018-05-10 18:29:00 32
 
< 0.1%
2018-05-07 12:31:00 21
 
< 0.1%
2018-05-02 15:15:00 16
 
< 0.1%
2018-07-24 16:07:00 16
 
< 0.1%
2018-07-17 14:16:00 15
 
< 0.1%
2018-05-16 13:44:00 15
 
< 0.1%
2018-08-03 15:10:00 15
 
< 0.1%
2018-08-08 15:01:00 15
 
< 0.1%
2018-06-08 14:40:00 14
 
< 0.1%
Other values (80739) 97171
98.0%
(Missing) 1735
 
1.8%

Length

2023-02-10T10:56:11.339072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2017-11-28 707
 
0.4%
2017-11-27 673
 
0.3%
2017-11-29 566
 
0.3%
2018-02-27 523
 
0.3%
2018-03-27 511
 
0.3%
2018-08-06 510
 
0.3%
2017-11-30 489
 
0.3%
2018-08-13 472
 
0.2%
2018-05-15 451
 
0.2%
2018-05-03 450
 
0.2%
Other values (37379) 189402
97.3%

Most occurring characters

ValueCountFrequency (%)
0 338077
18.3%
1 287701
15.6%
2 229707
12.4%
- 194754
10.5%
: 194754
10.5%
8 103071
 
5.6%
97377
 
5.3%
7 88674
 
4.8%
3 81737
 
4.4%
4 76783
 
4.2%
Other values (3) 157528
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1363278
73.7%
Dash Punctuation 194754
 
10.5%
Other Punctuation 194754
 
10.5%
Space Separator 97377
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 338077
24.8%
1 287701
21.1%
2 229707
16.8%
8 103071
 
7.6%
7 88674
 
6.5%
3 81737
 
6.0%
4 76783
 
5.6%
5 74515
 
5.5%
6 42553
 
3.1%
9 40460
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 194754
100.0%
Other Punctuation
ValueCountFrequency (%)
: 194754
100.0%
Space Separator
ValueCountFrequency (%)
97377
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1850163
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 338077
18.3%
1 287701
15.6%
2 229707
12.4%
- 194754
10.5%
: 194754
10.5%
8 103071
 
5.6%
97377
 
5.3%
7 88674
 
4.8%
3 81737
 
4.4%
4 76783
 
4.2%
Other values (3) 157528
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1850163
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 338077
18.3%
1 287701
15.6%
2 229707
12.4%
- 194754
10.5%
: 194754
10.5%
8 103071
 
5.6%
97377
 
5.3%
7 88674
 
4.8%
3 81737
 
4.4%
4 76783
 
4.2%
Other values (3) 157528
8.5%

order_delivered_customer_date
Categorical

HIGH CARDINALITY  MISSING  UNIFORM 

Distinct95394
Distinct (%)99.2%
Missing2908
Missing (%)2.9%
Memory size1.5 MiB
2018-05-14 20:02:44
 
3
2018-05-08 23:38:46
 
3
2018-05-08 19:36:48
 
3
2018-02-14 21:09:19
 
3
2017-06-19 18:47:51
 
3
Other values (95389)
96189 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1827876
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94591 ?
Unique (%)98.3%

Sample

1st row2017-10-10 21:25:13
2nd row2018-08-07 15:27:45
3rd row2018-08-17 18:06:29
4th row2017-12-02 00:28:42
5th row2018-02-16 18:17:02

Common Values

ValueCountFrequency (%)
2018-05-14 20:02:44 3
 
< 0.1%
2018-05-08 23:38:46 3
 
< 0.1%
2018-05-08 19:36:48 3
 
< 0.1%
2018-02-14 21:09:19 3
 
< 0.1%
2017-06-19 18:47:51 3
 
< 0.1%
2017-12-02 00:26:45 3
 
< 0.1%
2018-07-24 21:36:42 3
 
< 0.1%
2017-12-06 18:30:10 2
 
< 0.1%
2018-02-01 20:29:51 2
 
< 0.1%
2018-04-23 16:45:44 2
 
< 0.1%
Other values (95384) 96177
97.0%
(Missing) 2908
 
2.9%

Length

2023-02-10T10:56:11.461701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-08-27 446
 
0.2%
2018-08-13 442
 
0.2%
2018-05-14 434
 
0.2%
2018-05-21 431
 
0.2%
2018-05-18 425
 
0.2%
2018-04-11 413
 
0.2%
2017-12-11 412
 
0.2%
2018-07-03 410
 
0.2%
2018-05-03 409
 
0.2%
2017-06-19 405
 
0.2%
Other values (41614) 188181
97.8%

Most occurring characters

ValueCountFrequency (%)
1 281486
15.4%
0 280536
15.3%
2 243070
13.3%
- 192408
10.5%
: 192408
10.5%
8 113401
6.2%
96204
 
5.3%
3 88895
 
4.9%
7 88844
 
4.9%
4 83193
 
4.6%
Other values (3) 167431
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1346856
73.7%
Dash Punctuation 192408
 
10.5%
Other Punctuation 192408
 
10.5%
Space Separator 96204
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 281486
20.9%
0 280536
20.8%
2 243070
18.0%
8 113401
8.4%
3 88895
 
6.6%
7 88844
 
6.6%
4 83193
 
6.2%
5 77959
 
5.8%
6 47877
 
3.6%
9 41595
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 192408
100.0%
Other Punctuation
ValueCountFrequency (%)
: 192408
100.0%
Space Separator
ValueCountFrequency (%)
96204
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1827876
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 281486
15.4%
0 280536
15.3%
2 243070
13.3%
- 192408
10.5%
: 192408
10.5%
8 113401
6.2%
96204
 
5.3%
3 88895
 
4.9%
7 88844
 
4.9%
4 83193
 
4.6%
Other values (3) 167431
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1827876
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 281486
15.4%
0 280536
15.3%
2 243070
13.3%
- 192408
10.5%
: 192408
10.5%
8 113401
6.2%
96204
 
5.3%
3 88895
 
4.9%
7 88844
 
4.9%
4 83193
 
4.6%
Other values (3) 167431
9.2%
Distinct423
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2017-12-20 00:00:00
 
522
2018-03-12 00:00:00
 
516
2018-03-13 00:00:00
 
513
2018-05-29 00:00:00
 
513
2018-02-14 00:00:00
 
507
Other values (418)
96541 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1883128
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st row2017-10-18 00:00:00
2nd row2018-08-13 00:00:00
3rd row2018-09-04 00:00:00
4th row2017-12-15 00:00:00
5th row2018-02-26 00:00:00

Common Values

ValueCountFrequency (%)
2017-12-20 00:00:00 522
 
0.5%
2018-03-12 00:00:00 516
 
0.5%
2018-03-13 00:00:00 513
 
0.5%
2018-05-29 00:00:00 513
 
0.5%
2018-02-14 00:00:00 507
 
0.5%
2017-12-18 00:00:00 493
 
0.5%
2018-05-28 00:00:00 492
 
0.5%
2018-03-06 00:00:00 492
 
0.5%
2018-02-06 00:00:00 491
 
0.5%
2018-04-12 00:00:00 490
 
0.5%
Other values (413) 94083
94.9%

Length

2023-02-10T10:56:11.611083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00:00:00 99112
50.0%
2017-12-20 522
 
0.3%
2018-03-12 516
 
0.3%
2018-03-13 513
 
0.3%
2018-05-29 513
 
0.3%
2018-02-14 507
 
0.3%
2017-12-18 493
 
0.2%
2018-05-28 492
 
0.2%
2018-03-06 492
 
0.2%
2018-02-06 491
 
0.2%
Other values (414) 94573
47.7%

Most occurring characters

ValueCountFrequency (%)
0 820140
43.6%
- 198224
 
10.5%
: 198224
 
10.5%
1 169147
 
9.0%
2 154439
 
8.2%
99112
 
5.3%
8 82589
 
4.4%
7 60284
 
3.2%
3 26556
 
1.4%
5 20357
 
1.1%
Other values (3) 54056
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1387568
73.7%
Dash Punctuation 198224
 
10.5%
Other Punctuation 198224
 
10.5%
Space Separator 99112
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 820140
59.1%
1 169147
 
12.2%
2 154439
 
11.1%
8 82589
 
6.0%
7 60284
 
4.3%
3 26556
 
1.9%
5 20357
 
1.5%
6 19001
 
1.4%
4 18580
 
1.3%
9 16475
 
1.2%
Dash Punctuation
ValueCountFrequency (%)
- 198224
100.0%
Other Punctuation
ValueCountFrequency (%)
: 198224
100.0%
Space Separator
ValueCountFrequency (%)
99112
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1883128
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 820140
43.6%
- 198224
 
10.5%
: 198224
 
10.5%
1 169147
 
9.0%
2 154439
 
8.2%
99112
 
5.3%
8 82589
 
4.4%
7 60284
 
3.2%
3 26556
 
1.4%
5 20357
 
1.1%
Other values (3) 54056
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1883128
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 820140
43.6%
- 198224
 
10.5%
: 198224
 
10.5%
1 169147
 
9.0%
2 154439
 
8.2%
99112
 
5.3%
8 82589
 
4.4%
7 60284
 
3.2%
3 26556
 
1.4%
5 20357
 
1.1%
Other values (3) 54056
 
2.9%

review_score
Categorical

Distinct5
Distinct (%)< 0.1%
Missing763
Missing (%)0.8%
Memory size1.5 MiB
5.0
56853 
4.0
18987 
1.0
11274 
3.0
8112 
2.0
 
3123

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters295047
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row4.0
3rd row5.0
4th row5.0
5th row5.0

Common Values

ValueCountFrequency (%)
5.0 56853
57.4%
4.0 18987
 
19.2%
1.0 11274
 
11.4%
3.0 8112
 
8.2%
2.0 3123
 
3.2%
(Missing) 763
 
0.8%

Length

2023-02-10T10:56:11.740244image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-10T10:56:11.884540image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
5.0 56853
57.8%
4.0 18987
 
19.3%
1.0 11274
 
11.5%
3.0 8112
 
8.2%
2.0 3123
 
3.2%

Most occurring characters

ValueCountFrequency (%)
. 98349
33.3%
0 98349
33.3%
5 56853
19.3%
4 18987
 
6.4%
1 11274
 
3.8%
3 8112
 
2.7%
2 3123
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 196698
66.7%
Other Punctuation 98349
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 98349
50.0%
5 56853
28.9%
4 18987
 
9.7%
1 11274
 
5.7%
3 8112
 
4.1%
2 3123
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 98349
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 295047
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 98349
33.3%
0 98349
33.3%
5 56853
19.3%
4 18987
 
6.4%
1 11274
 
3.8%
3 8112
 
2.7%
2 3123
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 295047
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 98349
33.3%
0 98349
33.3%
5 56853
19.3%
4 18987
 
6.4%
1 11274
 
3.8%
3 8112
 
2.7%
2 3123
 
1.1%

length_comment_title
Real number (ℝ)

Distinct27
Distinct (%)< 0.1%
Missing763
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean1.3829017
Minimum0
Maximum26
Zeros86798
Zeros (%)87.6%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-10T10:56:12.007374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile12
Maximum26
Range26
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.3648976
Coefficient of variation (CV)3.1563325
Kurtosis11.62685
Mean1.3829017
Median Absolute Deviation (MAD)0
Skewness3.4511124
Sum136007
Variance19.052331
MonotonicityNot monotonic
2023-02-10T10:56:12.137496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 86798
87.6%
9 2000
 
2.0%
5 1117
 
1.1%
15 870
 
0.9%
3 703
 
0.7%
10 575
 
0.6%
13 489
 
0.5%
17 482
 
0.5%
25 429
 
0.4%
14 399
 
0.4%
Other values (17) 4487
 
4.5%
(Missing) 763
 
0.8%
ValueCountFrequency (%)
0 86798
87.6%
1 164
 
0.2%
2 253
 
0.3%
3 703
 
0.7%
4 178
 
0.2%
5 1117
 
1.1%
6 243
 
0.2%
7 388
 
0.4%
8 342
 
0.3%
9 2000
 
2.0%
ValueCountFrequency (%)
26 1
 
< 0.1%
25 429
0.4%
24 221
0.2%
23 213
0.2%
22 213
0.2%
21 239
0.2%
20 390
0.4%
19 268
0.3%
18 301
0.3%
17 482
0.5%

length_comment_message
Real number (ℝ)

Distinct209
Distinct (%)0.2%
Missing763
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean28.152091
Minimum0
Maximum208
Zeros57807
Zeros (%)58.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-10T10:56:12.288632image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q341
95-th percentile146
Maximum208
Range208
Interquartile range (IQR)41

Descriptive statistics

Standard deviation48.19114
Coefficient of variation (CV)1.7118139
Kurtosis3.346744
Mean28.152091
Median Absolute Deviation (MAD)0
Skewness1.9890494
Sum2768730
Variance2322.3859
MonotonicityNot monotonic
2023-02-10T10:56:12.440119image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 57807
58.3%
9 1003
 
1.0%
200 589
 
0.6%
5 556
 
0.6%
3 514
 
0.5%
26 499
 
0.5%
20 473
 
0.5%
10 469
 
0.5%
34 460
 
0.5%
31 448
 
0.5%
Other values (199) 35531
35.8%
(Missing) 763
 
0.8%
ValueCountFrequency (%)
0 57807
58.3%
1 97
 
0.1%
2 196
 
0.2%
3 514
 
0.5%
4 101
 
0.1%
5 556
 
0.6%
6 205
 
0.2%
7 235
 
0.2%
8 244
 
0.2%
9 1003
 
1.0%
ValueCountFrequency (%)
208 1
 
< 0.1%
207 1
 
< 0.1%
206 1
 
< 0.1%
205 1
 
< 0.1%
204 14
 
< 0.1%
203 17
 
< 0.1%
202 12
 
< 0.1%
201 22
 
< 0.1%
200 589
0.6%
199 333
0.3%
Distinct97644
Distinct (%)99.3%
Missing763
Missing (%)0.8%
Memory size1.5 MiB
Minimum2017-01-13 20:22:46
Maximum2018-10-29 12:27:35
2023-02-10T10:56:12.600888image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:12.756232image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

payment_type
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
credit_card
74007 
boleto
19721 
credit_card,voucher
 
2240
voucher
 
1615
debit_card
 
1525
Other values (2)
 
4

Length

Max length22
Median length11
Mean length10.105467
Min length6

Characters and Unicode

Total characters1001573
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowcredit_card,voucher
2nd rowboleto
3rd rowcredit_card
4th rowcredit_card
5th rowcredit_card

Common Values

ValueCountFrequency (%)
credit_card 74007
74.7%
boleto 19721
 
19.9%
credit_card,voucher 2240
 
2.3%
voucher 1615
 
1.6%
debit_card 1525
 
1.5%
not_defined 3
 
< 0.1%
credit_card,debit_card 1
 
< 0.1%

Length

2023-02-10T10:56:12.903440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-10T10:56:13.060310image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
credit_card 74007
74.7%
boleto 19721
 
19.9%
credit_card,voucher 2240
 
2.3%
voucher 1615
 
1.6%
debit_card 1525
 
1.5%
not_defined 3
 
< 0.1%
credit_card,debit_card 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
c 157877
15.8%
r 157877
15.8%
d 155554
15.5%
e 101356
10.1%
t 97498
9.7%
i 77777
7.8%
_ 77777
7.8%
a 77774
7.8%
o 43300
 
4.3%
b 21247
 
2.1%
Other values (7) 33536
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 921555
92.0%
Connector Punctuation 77777
 
7.8%
Other Punctuation 2241
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 157877
17.1%
r 157877
17.1%
d 155554
16.9%
e 101356
11.0%
t 97498
10.6%
i 77777
8.4%
a 77774
8.4%
o 43300
 
4.7%
b 21247
 
2.3%
l 19721
 
2.1%
Other values (5) 11574
 
1.3%
Connector Punctuation
ValueCountFrequency (%)
_ 77777
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2241
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 921555
92.0%
Common 80018
 
8.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 157877
17.1%
r 157877
17.1%
d 155554
16.9%
e 101356
11.0%
t 97498
10.6%
i 77777
8.4%
a 77774
8.4%
o 43300
 
4.7%
b 21247
 
2.3%
l 19721
 
2.1%
Other values (5) 11574
 
1.3%
Common
ValueCountFrequency (%)
_ 77777
97.2%
, 2241
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1001573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 157877
15.8%
r 157877
15.8%
d 155554
15.5%
e 101356
10.1%
t 97498
9.7%
i 77777
7.8%
_ 77777
7.8%
a 77774
7.8%
o 43300
 
4.3%
b 21247
 
2.1%
Other values (7) 33536
 
3.3%

payment_installments
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.928152
Minimum0
Maximum24
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-10T10:56:13.185569image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median2
Q34
95-th percentile10
Maximum24
Range24
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7140631
Coefficient of variation (CV)0.92688602
Kurtosis2.3666857
Mean2.928152
Median Absolute Deviation (MAD)1
Skewness1.6018438
Sum290215
Variance7.3661387
MonotonicityNot monotonic
2023-02-10T10:56:13.311078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 48140
48.6%
2 12332
 
12.4%
3 10385
 
10.5%
4 7044
 
7.1%
10 5273
 
5.3%
5 5207
 
5.3%
8 4248
 
4.3%
6 3890
 
3.9%
7 1609
 
1.6%
9 641
 
0.6%
Other values (14) 343
 
0.3%
ValueCountFrequency (%)
0 2
 
< 0.1%
1 48140
48.6%
2 12332
 
12.4%
3 10385
 
10.5%
4 7044
 
7.1%
5 5207
 
5.3%
6 3890
 
3.9%
7 1609
 
1.6%
8 4248
 
4.3%
9 641
 
0.6%
ValueCountFrequency (%)
24 18
 
< 0.1%
23 1
 
< 0.1%
22 1
 
< 0.1%
21 3
 
< 0.1%
20 17
 
< 0.1%
18 27
 
< 0.1%
17 8
 
< 0.1%
16 5
 
< 0.1%
15 74
0.1%
14 15
 
< 0.1%

payment_value
Real number (ℝ)

Distinct27892
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160.9241
Minimum0
Maximum13664.08
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-10T10:56:13.453987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile32.38
Q162.0075
median105.28
Q3176.88
95-th percentile452.2845
Maximum13664.08
Range13664.08
Interquartile range (IQR)114.8725

Descriptive statistics

Standard deviation222.00748
Coefficient of variation (CV)1.3795788
Kurtosis233.92317
Mean160.9241
Median Absolute Deviation (MAD)51.6
Skewness9.1659713
Sum15949510
Variance49287.321
MonotonicityNot monotonic
2023-02-10T10:56:13.603536image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77.57 254
 
0.3%
35 169
 
0.2%
73.34 163
 
0.2%
116.94 132
 
0.1%
56.78 124
 
0.1%
107.78 121
 
0.1%
65 117
 
0.1%
86.15 107
 
0.1%
99.9 106
 
0.1%
67.5 105
 
0.1%
Other values (27882) 97714
98.6%
ValueCountFrequency (%)
0 3
< 0.1%
9.59 1
 
< 0.1%
10.07 1
 
< 0.1%
10.89 1
 
< 0.1%
11.56 1
 
< 0.1%
11.62 1
 
< 0.1%
11.63 2
< 0.1%
12.22 1
 
< 0.1%
12.28 1
 
< 0.1%
12.39 1
 
< 0.1%
ValueCountFrequency (%)
13664.08 1
< 0.1%
7274.88 1
< 0.1%
6929.31 1
< 0.1%
6922.21 1
< 0.1%
6726.66 1
< 0.1%
6081.54 1
< 0.1%
4950.34 1
< 0.1%
4809.44 1
< 0.1%
4764.34 1
< 0.1%
4681.78 1
< 0.1%
Distinct31695
Distinct (%)32.2%
Missing758
Missing (%)0.8%
Memory size1.5 MiB
aca2eb7d00ea1a7b8ebd4e68314663af
 
429
99a4788cb24856965c36a24e339b6058
 
427
422879e10f46682990de24d770e7f83d
 
339
d1c427060a0f73f6b889a5c7c61f2ac4
 
311
53b36df67ebb7c41585e8d54d6772e08
 
303
Other values (31690)
96545 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3147328
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18852 ?
Unique (%)19.2%

Sample

1st row87285b34884572647811a353c7ac498a
2nd row595fac2a385ac33a80bd5114aec74eb8
3rd rowaa4383b373c6aca5d8797843e5594415
4th rowd0b61bfb1de832b15ba9d266ca96e5b0
5th row65266b2da20d04dbe00c5c2d3bb7859e

Common Values

ValueCountFrequency (%)
aca2eb7d00ea1a7b8ebd4e68314663af 429
 
0.4%
99a4788cb24856965c36a24e339b6058 427
 
0.4%
422879e10f46682990de24d770e7f83d 339
 
0.3%
d1c427060a0f73f6b889a5c7c61f2ac4 311
 
0.3%
53b36df67ebb7c41585e8d54d6772e08 303
 
0.3%
389d119b48cf3043d311335e499d9c6b 299
 
0.3%
368c6c730842d78016ad823897a372db 285
 
0.3%
154e7e31ebfa092203795c972e5804a6 269
 
0.3%
53759a2ecddad2bb87a079a1f1519f73 264
 
0.3%
2b4609f8948be18874494203496bc318 258
 
0.3%
Other values (31685) 95170
96.0%
(Missing) 758
 
0.8%

Length

2023-02-10T10:56:13.741178image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
aca2eb7d00ea1a7b8ebd4e68314663af 429
 
0.4%
99a4788cb24856965c36a24e339b6058 427
 
0.4%
422879e10f46682990de24d770e7f83d 339
 
0.3%
d1c427060a0f73f6b889a5c7c61f2ac4 311
 
0.3%
53b36df67ebb7c41585e8d54d6772e08 303
 
0.3%
389d119b48cf3043d311335e499d9c6b 299
 
0.3%
368c6c730842d78016ad823897a372db 285
 
0.3%
154e7e31ebfa092203795c972e5804a6 269
 
0.3%
53759a2ecddad2bb87a079a1f1519f73 264
 
0.3%
2b4609f8948be18874494203496bc318 258
 
0.3%
Other values (31685) 95170
96.8%

Most occurring characters

ValueCountFrequency (%)
3 202346
 
6.4%
9 199979
 
6.4%
8 198473
 
6.3%
e 198357
 
6.3%
7 197610
 
6.3%
4 197555
 
6.3%
a 197524
 
6.3%
0 197277
 
6.3%
c 196851
 
6.3%
5 196311
 
6.2%
Other values (6) 1165045
37.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1976192
62.8%
Lowercase Letter 1171136
37.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 202346
10.2%
9 199979
10.1%
8 198473
10.0%
7 197610
10.0%
4 197555
10.0%
0 197277
10.0%
5 196311
9.9%
2 196289
9.9%
6 195418
9.9%
1 194934
9.9%
Lowercase Letter
ValueCountFrequency (%)
e 198357
16.9%
a 197524
16.9%
c 196851
16.8%
b 194783
16.6%
d 193184
16.5%
f 190437
16.3%

Most occurring scripts

ValueCountFrequency (%)
Common 1976192
62.8%
Latin 1171136
37.2%

Most frequent character per script

Common
ValueCountFrequency (%)
3 202346
10.2%
9 199979
10.1%
8 198473
10.0%
7 197610
10.0%
4 197555
10.0%
0 197277
10.0%
5 196311
9.9%
2 196289
9.9%
6 195418
9.9%
1 194934
9.9%
Latin
ValueCountFrequency (%)
e 198357
16.9%
a 197524
16.9%
c 196851
16.8%
b 194783
16.6%
d 193184
16.5%
f 190437
16.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3147328
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 202346
 
6.4%
9 199979
 
6.4%
8 198473
 
6.3%
e 198357
 
6.3%
7 197610
 
6.3%
4 197555
 
6.3%
a 197524
 
6.3%
0 197277
 
6.3%
c 196851
 
6.3%
5 196311
 
6.2%
Other values (6) 1165045
37.0%

nb_items
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing758
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean1.1415906
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-10T10:56:13.851382image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum21
Range20
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.53809943
Coefficient of variation (CV)0.47135938
Kurtosis115.34737
Mean1.1415906
Median Absolute Deviation (MAD)0
Skewness7.5401727
Sum112280
Variance0.289551
MonotonicityNot monotonic
2023-02-10T10:56:13.968462image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 88587
89.4%
2 7491
 
7.6%
3 1317
 
1.3%
4 502
 
0.5%
5 203
 
0.2%
6 196
 
0.2%
7 22
 
< 0.1%
10 8
 
< 0.1%
8 8
 
< 0.1%
12 5
 
< 0.1%
Other values (7) 15
 
< 0.1%
(Missing) 758
 
0.8%
ValueCountFrequency (%)
1 88587
89.4%
2 7491
 
7.6%
3 1317
 
1.3%
4 502
 
0.5%
5 203
 
0.2%
6 196
 
0.2%
7 22
 
< 0.1%
8 8
 
< 0.1%
9 3
 
< 0.1%
10 8
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 2
 
< 0.1%
15 2
 
< 0.1%
14 2
 
< 0.1%
13 1
 
< 0.1%
12 5
< 0.1%
11 4
< 0.1%
10 8
< 0.1%
9 3
 
< 0.1%
8 8
< 0.1%

sum_price
Real number (ℝ)

Distinct7751
Distinct (%)7.9%
Missing758
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean137.68487
Minimum0.85
Maximum13440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-10T10:56:14.119187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.85
5-th percentile19
Q145.9
median86.9
Q3149.9
95-th percentile399.9
Maximum13440
Range13439.15
Interquartile range (IQR)104

Descriptive statistics

Standard deviation210.68338
Coefficient of variation (CV)1.5301854
Kurtosis266.69704
Mean137.68487
Median Absolute Deviation (MAD)47.9
Skewness9.7453475
Sum13541858
Variance44387.486
MonotonicityNot monotonic
2023-02-10T10:56:14.289765image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.9 1712
 
1.7%
69.9 1601
 
1.6%
49.9 1411
 
1.4%
89.9 1241
 
1.3%
99.9 1186
 
1.2%
79.9 1004
 
1.0%
39.9 975
 
1.0%
29.9 958
 
1.0%
19.9 911
 
0.9%
29.99 870
 
0.9%
Other values (7741) 86485
87.3%
ValueCountFrequency (%)
0.85 2
< 0.1%
2.2 1
 
< 0.1%
2.29 1
 
< 0.1%
2.9 1
 
< 0.1%
2.99 1
 
< 0.1%
3 2
< 0.1%
3.49 1
 
< 0.1%
3.5 2
< 0.1%
3.54 1
 
< 0.1%
3.85 3
< 0.1%
ValueCountFrequency (%)
13440 1
< 0.1%
7160 1
< 0.1%
6735 1
< 0.1%
6729 1
< 0.1%
6499 1
< 0.1%
5934.6 1
< 0.1%
4799 1
< 0.1%
4690 1
< 0.1%
4599.9 1
< 0.1%
4590 1
< 0.1%

sum_freight_value
Real number (ℝ)

Distinct7954
Distinct (%)8.1%
Missing758
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean22.820752
Minimum0
Maximum1794.96
Zeros338
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-10T10:56:14.456322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7.88
Q113.85
median17.17
Q324.03
95-th percentile54.92
Maximum1794.96
Range1794.96
Interquartile range (IQR)10.18

Descriptive statistics

Standard deviation21.656041
Coefficient of variation (CV)0.94896265
Kurtosis566.55997
Mean22.820752
Median Absolute Deviation (MAD)4.38
Skewness12.072998
Sum2244512.2
Variance468.98413
MonotonicityNot monotonic
2023-02-10T10:56:14.614823image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.1 2952
 
3.0%
7.78 1839
 
1.9%
14.1 1529
 
1.5%
11.85 1444
 
1.5%
18.23 1219
 
1.2%
7.39 1137
 
1.1%
15.23 823
 
0.8%
16.11 794
 
0.8%
8.72 761
 
0.8%
16.79 697
 
0.7%
Other values (7944) 85159
85.9%
(Missing) 758
 
0.8%
ValueCountFrequency (%)
0 338
0.3%
5.7 1
 
< 0.1%
5.82 1
 
< 0.1%
5.88 2
 
< 0.1%
6.52 1
 
< 0.1%
6.53 2
 
< 0.1%
6.56 1
 
< 0.1%
6.57 5
 
< 0.1%
6.78 5
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
1794.96 1
< 0.1%
1002.29 1
< 0.1%
711.33 1
< 0.1%
626.64 1
< 0.1%
502.98 1
< 0.1%
497.42 1
< 0.1%
497.08 1
< 0.1%
479.28 1
< 0.1%
458.73 1
< 0.1%
456.47 1
< 0.1%

customer_unique_id
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct95780
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
8d50f5eadf50201ccdcedfb9e2ac8455
 
17
3e43e6105506432c953e165fb2acf44c
 
9
1b6c7548a2a1f9037c1fd3ddfed95f33
 
7
6469f99c1f9dfae7733b25662e7f1782
 
7
ca77025e7201e3b30c44b472ff346268
 
7
Other values (95775)
99065 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters3171584
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique92795 ?
Unique (%)93.6%

Sample

1st row7c396fd4830fd04220f754e42b4e5bff
2nd rowaf07308b275d755c9edb36a90c618231
3rd row3a653a41f6f9fc3d2a113cf8398680e8
4th row7c142cf63193a1473d2e66489a9ae977
5th row72632f0f9dd73dfee390c9b22eb56dd6

Common Values

ValueCountFrequency (%)
8d50f5eadf50201ccdcedfb9e2ac8455 17
 
< 0.1%
3e43e6105506432c953e165fb2acf44c 9
 
< 0.1%
1b6c7548a2a1f9037c1fd3ddfed95f33 7
 
< 0.1%
6469f99c1f9dfae7733b25662e7f1782 7
 
< 0.1%
ca77025e7201e3b30c44b472ff346268 7
 
< 0.1%
f0e310a6839dce9de1638e0fe5ab282a 6
 
< 0.1%
de34b16117594161a6a89c50b289d35a 6
 
< 0.1%
dc813062e0fc23409cd255f7f53c7074 6
 
< 0.1%
12f5d6e1cbf93dafd9dcc19095df0b3d 6
 
< 0.1%
47c1a3033b8b77b3ab6e109eb4d5fdf3 6
 
< 0.1%
Other values (95770) 99035
99.9%

Length

2023-02-10T10:56:14.763864image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
8d50f5eadf50201ccdcedfb9e2ac8455 17
 
< 0.1%
3e43e6105506432c953e165fb2acf44c 9
 
< 0.1%
1b6c7548a2a1f9037c1fd3ddfed95f33 7
 
< 0.1%
ca77025e7201e3b30c44b472ff346268 7
 
< 0.1%
6469f99c1f9dfae7733b25662e7f1782 7
 
< 0.1%
f0e310a6839dce9de1638e0fe5ab282a 6
 
< 0.1%
de34b16117594161a6a89c50b289d35a 6
 
< 0.1%
dc813062e0fc23409cd255f7f53c7074 6
 
< 0.1%
12f5d6e1cbf93dafd9dcc19095df0b3d 6
 
< 0.1%
47c1a3033b8b77b3ab6e109eb4d5fdf3 6
 
< 0.1%
Other values (95770) 99035
99.9%

Most occurring characters

ValueCountFrequency (%)
6 198691
 
6.3%
8 198678
 
6.3%
1 198671
 
6.3%
a 198460
 
6.3%
d 198441
 
6.3%
b 198367
 
6.3%
0 198366
 
6.3%
5 198350
 
6.3%
2 198265
 
6.3%
e 198253
 
6.3%
Other values (6) 1187042
37.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1982483
62.5%
Lowercase Letter 1189101
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 198691
10.0%
8 198678
10.0%
1 198671
10.0%
0 198366
10.0%
5 198350
10.0%
2 198265
10.0%
9 198203
10.0%
3 197987
10.0%
4 197737
10.0%
7 197535
10.0%
Lowercase Letter
ValueCountFrequency (%)
a 198460
16.7%
d 198441
16.7%
b 198367
16.7%
e 198253
16.7%
f 197959
16.6%
c 197621
16.6%

Most occurring scripts

ValueCountFrequency (%)
Common 1982483
62.5%
Latin 1189101
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
6 198691
10.0%
8 198678
10.0%
1 198671
10.0%
0 198366
10.0%
5 198350
10.0%
2 198265
10.0%
9 198203
10.0%
3 197987
10.0%
4 197737
10.0%
7 197535
10.0%
Latin
ValueCountFrequency (%)
a 198460
16.7%
d 198441
16.7%
b 198367
16.7%
e 198253
16.7%
f 197959
16.6%
c 197621
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3171584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 198691
 
6.3%
8 198678
 
6.3%
1 198671
 
6.3%
a 198460
 
6.3%
d 198441
 
6.3%
b 198367
 
6.3%
0 198366
 
6.3%
5 198350
 
6.3%
2 198265
 
6.3%
e 198253
 
6.3%
Other values (6) 1187042
37.4%

customer_city
Categorical

Distinct4116
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
sao paulo
15504 
rio de janeiro
 
6844
belo horizonte
 
2761
brasilia
 
2125
curitiba
 
1515
Other values (4111)
70363 

Length

Max length32
Median length27
Mean length10.342945
Min length3

Characters and Unicode

Total characters1025110
Distinct characters31
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1144 ?
Unique (%)1.2%

Sample

1st rowsao paulo
2nd rowbarreiras
3rd rowvianopolis
4th rowsao goncalo do amarante
5th rowsanto andre

Common Values

ValueCountFrequency (%)
sao paulo 15504
 
15.6%
rio de janeiro 6844
 
6.9%
belo horizonte 2761
 
2.8%
brasilia 2125
 
2.1%
curitiba 1515
 
1.5%
campinas 1437
 
1.4%
porto alegre 1372
 
1.4%
salvador 1245
 
1.3%
guarulhos 1188
 
1.2%
sao bernardo do campo 935
 
0.9%
Other values (4106) 64186
64.8%

Length

2023-02-10T10:56:14.901227image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sao 20990
 
12.1%
paulo 15570
 
9.0%
de 9633
 
5.5%
rio 8237
 
4.7%
janeiro 6844
 
3.9%
do 4260
 
2.5%
belo 2820
 
1.6%
horizonte 2786
 
1.6%
brasilia 2134
 
1.2%
porto 1641
 
0.9%
Other values (3282) 98785
56.9%

Most occurring characters

ValueCountFrequency (%)
a 169060
16.5%
o 126099
12.3%
i 78475
 
7.7%
r 76234
 
7.4%
74588
 
7.3%
e 66777
 
6.5%
s 62696
 
6.1%
n 45532
 
4.4%
u 44786
 
4.4%
l 44678
 
4.4%
Other values (21) 236185
23.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 950063
92.7%
Space Separator 74588
 
7.3%
Dash Punctuation 231
 
< 0.1%
Other Punctuation 226
 
< 0.1%
Decimal Number 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 169060
17.8%
o 126099
13.3%
i 78475
 
8.3%
r 76234
 
8.0%
e 66777
 
7.0%
s 62696
 
6.6%
n 45532
 
4.8%
u 44786
 
4.7%
l 44678
 
4.7%
p 37012
 
3.9%
Other values (16) 198714
20.9%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
4 1
50.0%
Space Separator
ValueCountFrequency (%)
74588
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 231
100.0%
Other Punctuation
ValueCountFrequency (%)
' 226
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 950063
92.7%
Common 75047
 
7.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 169060
17.8%
o 126099
13.3%
i 78475
 
8.3%
r 76234
 
8.0%
e 66777
 
7.0%
s 62696
 
6.6%
n 45532
 
4.8%
u 44786
 
4.7%
l 44678
 
4.7%
p 37012
 
3.9%
Other values (16) 198714
20.9%
Common
ValueCountFrequency (%)
74588
99.4%
- 231
 
0.3%
' 226
 
0.3%
1 1
 
< 0.1%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1025110
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 169060
16.5%
o 126099
12.3%
i 78475
 
7.7%
r 76234
 
7.4%
74588
 
7.3%
e 66777
 
6.5%
s 62696
 
6.1%
n 45532
 
4.4%
u 44786
 
4.4%
l 44678
 
4.4%
Other values (21) 236185
23.0%

customer_state
Categorical

Distinct27
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
SP
41631 
RJ
12796 
MG
11595 
RS
5441 
PR
5025 
Other values (22)
22624 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters198224
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSP
2nd rowBA
3rd rowGO
4th rowRN
5th rowSP

Common Values

ValueCountFrequency (%)
SP 41631
42.0%
RJ 12796
 
12.9%
MG 11595
 
11.7%
RS 5441
 
5.5%
PR 5025
 
5.1%
SC 3626
 
3.7%
BA 3376
 
3.4%
DF 2134
 
2.2%
ES 2029
 
2.0%
GO 2011
 
2.0%
Other values (17) 9448
 
9.5%

Length

2023-02-10T10:56:15.030574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
sp 41631
42.0%
rj 12796
 
12.9%
mg 11595
 
11.7%
rs 5441
 
5.5%
pr 5025
 
5.1%
sc 3626
 
3.7%
ba 3376
 
3.4%
df 2134
 
2.2%
es 2029
 
2.0%
go 2011
 
2.0%
Other values (17) 9448
 
9.5%

Most occurring characters

ValueCountFrequency (%)
S 53789
27.1%
P 50369
25.4%
R 24084
12.1%
M 14105
 
7.1%
G 13606
 
6.9%
J 12796
 
6.5%
A 5798
 
2.9%
E 5349
 
2.7%
C 5035
 
2.5%
B 3911
 
2.0%
Other values (7) 9382
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 198224
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 53789
27.1%
P 50369
25.4%
R 24084
12.1%
M 14105
 
7.1%
G 13606
 
6.9%
J 12796
 
6.5%
A 5798
 
2.9%
E 5349
 
2.7%
C 5035
 
2.5%
B 3911
 
2.0%
Other values (7) 9382
 
4.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 198224
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 53789
27.1%
P 50369
25.4%
R 24084
12.1%
M 14105
 
7.1%
G 13606
 
6.9%
J 12796
 
6.5%
A 5798
 
2.9%
E 5349
 
2.7%
C 5035
 
2.5%
B 3911
 
2.0%
Other values (7) 9382
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 198224
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 53789
27.1%
P 50369
25.4%
R 24084
12.1%
M 14105
 
7.1%
G 13606
 
6.9%
J 12796
 
6.5%
A 5798
 
2.9%
E 5349
 
2.7%
C 5035
 
2.5%
B 3911
 
2.0%
Other values (7) 9382
 
4.7%
Distinct2952
Distinct (%)3.0%
Missing758
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean782.43398
Minimum0
Maximum3992
Zeros1418
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-10T10:56:15.174189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile138
Q1341
median600
Q3986
95-th percentile2120
Maximum3992
Range3992
Interquartile range (IQR)645

Descriptive statistics

Standard deviation656.64195
Coefficient of variation (CV)0.83922985
Kurtosis4.8014892
Mean782.43398
Median Absolute Deviation (MAD)300
Skewness1.9732056
Sum76955512
Variance431178.66
MonotonicityNot monotonic
2023-02-10T10:56:15.769940image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1418
 
1.4%
1893 583
 
0.6%
492 539
 
0.5%
341 536
 
0.5%
903 479
 
0.5%
245 477
 
0.5%
348 458
 
0.5%
236 428
 
0.4%
366 394
 
0.4%
575 361
 
0.4%
Other values (2942) 92681
93.5%
(Missing) 758
 
0.8%
ValueCountFrequency (%)
0 1418
1.4%
4 6
 
< 0.1%
8 1
 
< 0.1%
15 1
 
< 0.1%
20 6
 
< 0.1%
26 2
 
< 0.1%
27 3
 
< 0.1%
28 2
 
< 0.1%
30 7
 
< 0.1%
31 2
 
< 0.1%
ValueCountFrequency (%)
3992 2
 
< 0.1%
3988 1
 
< 0.1%
3985 3
< 0.1%
3976 3
< 0.1%
3963 1
 
< 0.1%
3956 2
 
< 0.1%
3954 2
 
< 0.1%
3950 1
 
< 0.1%
3948 1
 
< 0.1%
3947 6
< 0.1%

product_photos_qty
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing758
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean2.217012
Minimum0
Maximum20
Zeros1418
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-10T10:56:15.918834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum20
Range20
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7528737
Coefficient of variation (CV)0.7906469
Kurtosis4.4503856
Mean2.217012
Median Absolute Deviation (MAD)1
Skewness1.8254914
Sum218052
Variance3.0725661
MonotonicityNot monotonic
2023-02-10T10:56:16.056147image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 47922
48.4%
2 19079
 
19.2%
3 11176
 
11.3%
4 7558
 
7.6%
5 4968
 
5.0%
6 3384
 
3.4%
0 1418
 
1.4%
7 1402
 
1.4%
8 676
 
0.7%
10 320
 
0.3%
Other values (10) 451
 
0.5%
(Missing) 758
 
0.8%
ValueCountFrequency (%)
0 1418
 
1.4%
1 47922
48.4%
2 19079
 
19.2%
3 11176
 
11.3%
4 7558
 
7.6%
5 4968
 
5.0%
6 3384
 
3.4%
7 1402
 
1.4%
8 676
 
0.7%
9 289
 
0.3%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 2
 
< 0.1%
18 4
 
< 0.1%
17 8
 
< 0.1%
15 12
 
< 0.1%
14 6
 
< 0.1%
13 26
 
< 0.1%
12 44
 
< 0.1%
11 59
 
0.1%
10 320
0.3%

product_weight_g
Real number (ℝ)

Distinct2182
Distinct (%)2.2%
Missing774
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean2100.4419
Minimum0
Maximum40425
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-10T10:56:16.235476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile125
Q1300
median700
Q31800
95-th percentile9750
Maximum40425
Range40425
Interquartile range (IQR)1500

Descriptive statistics

Standard deviation3761.5403
Coefficient of variation (CV)1.7908328
Kurtosis16.409562
Mean2100.4419
Median Absolute Deviation (MAD)500
Skewness3.6098005
Sum2.0655326 × 108
Variance14149185
MonotonicityNot monotonic
2023-02-10T10:56:16.398546image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 5908
 
6.0%
150 4631
 
4.7%
250 3992
 
4.0%
300 3717
 
3.8%
400 3172
 
3.2%
100 3100
 
3.1%
350 2819
 
2.8%
500 2356
 
2.4%
600 2278
 
2.3%
700 1743
 
1.8%
Other values (2172) 64622
65.2%
ValueCountFrequency (%)
0 6
 
< 0.1%
2 5
 
< 0.1%
25 3
 
< 0.1%
50 841
0.8%
53 2
 
< 0.1%
54 1
 
< 0.1%
55 2
 
< 0.1%
58 1
 
< 0.1%
60 8
 
< 0.1%
61 4
 
< 0.1%
ValueCountFrequency (%)
40425 3
 
< 0.1%
30000 254
0.3%
29800 1
 
< 0.1%
29750 1
 
< 0.1%
29700 3
 
< 0.1%
29600 5
 
< 0.1%
29500 1
 
< 0.1%
29250 1
 
< 0.1%
29150 1
 
< 0.1%
29100 1
 
< 0.1%

product_length_cm
Real number (ℝ)

Distinct99
Distinct (%)0.1%
Missing774
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean30.096321
Minimum7
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-10T10:56:16.571990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile16
Q118
median25
Q338
95-th percentile61.15
Maximum105
Range98
Interquartile range (IQR)20

Descriptive statistics

Standard deviation16.125012
Coefficient of variation (CV)0.53578016
Kurtosis3.7916737
Mean30.096321
Median Absolute Deviation (MAD)8
Skewness1.7711629
Sum2959612
Variance260.016
MonotonicityNot monotonic
2023-02-10T10:56:16.730032image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 15238
 
15.4%
20 9112
 
9.2%
30 6309
 
6.4%
17 5328
 
5.4%
18 5153
 
5.2%
19 4133
 
4.2%
25 4121
 
4.2%
40 3557
 
3.6%
22 3415
 
3.4%
35 2574
 
2.6%
Other values (89) 39398
39.8%
ValueCountFrequency (%)
7 30
 
< 0.1%
8 2
 
< 0.1%
9 4
 
< 0.1%
10 7
 
< 0.1%
11 82
 
0.1%
12 34
 
< 0.1%
13 49
 
< 0.1%
14 119
 
0.1%
15 178
 
0.2%
16 15238
15.4%
ValueCountFrequency (%)
105 300
0.3%
104 29
 
< 0.1%
103 35
 
< 0.1%
102 42
 
< 0.1%
101 88
 
0.1%
100 308
0.3%
99 33
 
< 0.1%
98 42
 
< 0.1%
97 10
 
< 0.1%
96 8
 
< 0.1%

product_height_cm
Real number (ℝ)

Distinct102
Distinct (%)0.1%
Missing774
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean16.470215
Minimum2
Maximum105
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-10T10:56:16.886230image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q18
median13
Q320
95-th percentile44
Maximum105
Range103
Interquartile range (IQR)12

Descriptive statistics

Standard deviation13.306655
Coefficient of variation (CV)0.80792234
Kurtosis7.4876906
Mean16.470215
Median Absolute Deviation (MAD)6
Skewness2.2594422
Sum1619648
Variance177.06706
MonotonicityNot monotonic
2023-02-10T10:56:17.041323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 8500
 
8.6%
20 5823
 
5.9%
15 5652
 
5.7%
12 5629
 
5.7%
11 5471
 
5.5%
2 4432
 
4.5%
4 4218
 
4.3%
8 4050
 
4.1%
16 3977
 
4.0%
5 3911
 
3.9%
Other values (92) 46675
47.1%
ValueCountFrequency (%)
2 4432
4.5%
3 2335
 
2.4%
4 4218
4.3%
5 3911
3.9%
6 3012
 
3.0%
7 3702
3.7%
8 4050
4.1%
9 2796
 
2.8%
10 8500
8.6%
11 5471
5.5%
ValueCountFrequency (%)
105 109
0.1%
104 12
 
< 0.1%
103 37
 
< 0.1%
102 7
 
< 0.1%
100 39
 
< 0.1%
99 5
 
< 0.1%
98 3
 
< 0.1%
97 1
 
< 0.1%
96 8
 
< 0.1%
95 21
 
< 0.1%

product_width_cm
Real number (ℝ)

Distinct94
Distinct (%)0.1%
Missing774
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean23.019169
Minimum6
Maximum118
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2023-02-10T10:56:17.210138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q115
median20
Q330
95-th percentile45
Maximum118
Range112
Interquartile range (IQR)15

Descriptive statistics

Standard deviation11.748029
Coefficient of variation (CV)0.51035851
Kurtosis4.6191154
Mean23.019169
Median Absolute Deviation (MAD)6
Skewness1.7215798
Sum2263659
Variance138.01618
MonotonicityNot monotonic
2023-02-10T10:56:17.364260image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 10436
 
10.5%
11 9163
 
9.2%
15 7891
 
8.0%
16 7355
 
7.4%
30 6415
 
6.5%
12 4839
 
4.9%
13 4676
 
4.7%
14 4069
 
4.1%
18 3554
 
3.6%
40 3374
 
3.4%
Other values (84) 36566
36.9%
ValueCountFrequency (%)
6 2
 
< 0.1%
7 5
 
< 0.1%
8 16
 
< 0.1%
9 48
 
< 0.1%
10 67
 
0.1%
11 9163
9.2%
12 4839
4.9%
13 4676
4.7%
14 4069
4.1%
15 7891
8.0%
ValueCountFrequency (%)
118 7
 
< 0.1%
105 14
 
< 0.1%
104 1
 
< 0.1%
103 1
 
< 0.1%
102 2
 
< 0.1%
101 2
 
< 0.1%
100 41
< 0.1%
98 1
 
< 0.1%
97 1
 
< 0.1%
95 2
 
< 0.1%
Distinct72
Distinct (%)0.1%
Missing758
Missing (%)0.8%
Memory size1.5 MiB
bed_bath_table
9297 
health_beauty
8759 
sports_leisure
7662 
computers_accessories
6641 
furniture_decor
6307 
Other values (67)
59688 

Length

Max length39
Median length31
Mean length12.772251
Min length3

Characters and Unicode

Total characters1256202
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhousewares
2nd rowperfumery
3rd rowauto
4th rowpet_shop
5th rowstationery

Common Values

ValueCountFrequency (%)
bed_bath_table 9297
 
9.4%
health_beauty 8759
 
8.8%
sports_leisure 7662
 
7.7%
computers_accessories 6641
 
6.7%
furniture_decor 6307
 
6.4%
housewares 5811
 
5.9%
watches_gifts 5602
 
5.7%
telephony 4179
 
4.2%
auto 3867
 
3.9%
toys 3826
 
3.9%
Other values (62) 36403
36.7%

Length

2023-02-10T10:56:17.536513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bed_bath_table 9297
 
9.5%
health_beauty 8759
 
8.9%
sports_leisure 7662
 
7.8%
computers_accessories 6641
 
6.8%
furniture_decor 6307
 
6.4%
housewares 5811
 
5.9%
watches_gifts 5602
 
5.7%
telephony 4179
 
4.2%
auto 3867
 
3.9%
toys 3826
 
3.9%
Other values (62) 36403
37.0%

Most occurring characters

ValueCountFrequency (%)
e 152199
12.1%
s 119339
 
9.5%
t 110624
 
8.8%
o 93377
 
7.4%
a 85428
 
6.8%
r 84089
 
6.7%
_ 83219
 
6.6%
u 64498
 
5.1%
c 59606
 
4.7%
i 51809
 
4.1%
Other values (15) 352014
28.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1172727
93.4%
Connector Punctuation 83219
 
6.6%
Decimal Number 256
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 152199
13.0%
s 119339
 
10.2%
t 110624
 
9.4%
o 93377
 
8.0%
a 85428
 
7.3%
r 84089
 
7.2%
u 64498
 
5.5%
c 59606
 
5.1%
i 51809
 
4.4%
h 50185
 
4.3%
Other values (13) 301573
25.7%
Connector Punctuation
ValueCountFrequency (%)
_ 83219
100.0%
Decimal Number
ValueCountFrequency (%)
2 256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1172727
93.4%
Common 83475
 
6.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 152199
13.0%
s 119339
 
10.2%
t 110624
 
9.4%
o 93377
 
8.0%
a 85428
 
7.3%
r 84089
 
7.2%
u 64498
 
5.5%
c 59606
 
5.1%
i 51809
 
4.4%
h 50185
 
4.3%
Other values (13) 301573
25.7%
Common
ValueCountFrequency (%)
_ 83219
99.7%
2 256
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1256202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 152199
12.1%
s 119339
 
9.5%
t 110624
 
8.8%
o 93377
 
7.4%
a 85428
 
6.8%
r 84089
 
6.7%
_ 83219
 
6.6%
u 64498
 
5.1%
c 59606
 
4.7%
i 51809
 
4.1%
Other values (15) 352014
28.0%

Interactions

2023-02-10T10:56:05.779071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:39.420184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:41.596157image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:43.844851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:46.127841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:48.373875image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:50.694513image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:53.063182image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:55.172189image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:57.282422image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:59.363910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:01.428574image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:03.890834image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:05.952832image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:39.564473image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:41.823184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:44.017910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:46.273150image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:48.537423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:50.842421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:53.211088image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:55.328609image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:57.428242image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:59.507041image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:01.667056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:04.026331image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:06.104051image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:39.706879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:41.977550image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:44.255952image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:46.411923image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:48.714467image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:51.020845image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:53.354531image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:55.467366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:57.572694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:59.652673image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:01.827619image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:04.155936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:06.237564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:39.849277image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:42.119063image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:44.418264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:46.584812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:48.864172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:51.273767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:53.507626image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:55.617415image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:57.719828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:59.803505image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:02.303694image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:04.290056image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:06.387375image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:39.994412image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:42.265804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:44.674756image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:46.758138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:49.019305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:51.535006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:53.666122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:55.769881image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:57.879407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:59.968605image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:02.553955image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:04.436579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:06.539014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:40.369341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:42.465311image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:44.825572image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:46.983759image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:49.174729image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:51.807101image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:53.830606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:55.951327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:58.040172image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:00.132558image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:02.705645image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:04.581779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:06.692661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:40.519282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:42.647780image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:44.982874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:47.191131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:49.324848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:51.973808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:53.983454image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:56.107084image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:58.227067image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:00.306468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:02.855248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:04.721006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:06.840312image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:40.677333image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:42.831138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:45.145527image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:47.353556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:49.498115image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:52.136767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:54.133231image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:56.272858image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:58.425476image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:00.472755image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:03.013083image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:04.871833image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:07.018156image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:40.835486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:43.022308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:45.300547image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:47.529943image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:49.677130image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:52.302341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:54.299703image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:56.425987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:58.588486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:00.638072image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:03.166698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:05.038950image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:07.229980image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:40.998997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:43.219196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:45.455458image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:47.704270image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:49.828468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:52.458850image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:54.460437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:56.602927image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:58.745229image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:00.803746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:03.342272image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:05.195985image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:07.392597image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:41.152657image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:43.395443image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:45.616217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:47.880607image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:49.989475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:52.621036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:54.620939image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:56.770274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:58.903186image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:00.967002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:03.503197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:05.347026image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:07.528910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:41.286318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:43.554798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:45.748420image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:48.045023image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:50.124425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:52.767137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:54.791080image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:56.940862image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:59.041191image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:01.106954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:03.625699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:05.480844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:07.668767image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:41.432318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:43.700889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:45.910065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:48.223798image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:50.545336image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:52.912485image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:54.992630image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:57.135264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:55:59.194249image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:01.259541image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:03.760037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-10T10:56:05.614210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-02-10T10:56:17.703146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
length_comment_titlelength_comment_messagepayment_installmentspayment_valuenb_itemssum_pricesum_freight_valueproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmorder_statusreview_scorepayment_typecustomer_stateproduct_category_name_english
length_comment_title1.0000.3150.0040.0340.0260.0280.0530.0310.006-0.011-0.032-0.003-0.0200.0220.0800.0280.0130.034
length_comment_message0.3151.0000.0440.0680.0820.0620.070-0.008-0.0050.0370.0130.0220.0150.0460.2050.0080.0210.020
payment_installments0.0040.0441.0000.3810.0570.3750.2310.0370.0030.2200.1190.1220.1370.0050.0200.1820.0330.090
payment_value0.0340.0680.3811.0000.2210.9900.5660.1930.0080.5190.2680.3470.2750.0120.0140.0060.0150.101
nb_items0.0260.0820.0570.2211.0000.1770.377-0.036-0.056-0.0050.0080.0040.0010.0000.0310.0090.0000.027
sum_price0.0280.0620.3750.9900.1771.0000.4690.1960.0120.5060.2560.3390.2650.0110.0120.0080.0130.092
sum_freight_value0.0530.0700.2310.5660.3770.4691.0000.100-0.0100.4190.2730.2720.2620.0030.0150.0000.0300.054
product_description_lenght0.031-0.0080.0370.193-0.0360.1960.1001.0000.1550.100-0.0110.132-0.0600.0050.0110.0120.0190.212
product_photos_qty0.006-0.0050.0030.008-0.0560.012-0.0100.1551.0000.0140.009-0.068-0.0040.0150.0110.0000.0130.150
product_weight_g-0.0110.0370.2200.519-0.0050.5060.4190.1000.0141.0000.6200.5360.6220.0060.0190.0110.0120.192
product_length_cm-0.0320.0130.1190.2680.0080.2560.273-0.0110.0090.6201.0000.2600.6390.0090.0140.0120.0110.260
product_height_cm-0.0030.0220.1220.3470.0040.3390.2720.132-0.0680.5360.2601.0000.3450.0130.0140.0110.0130.267
product_width_cm-0.0200.0150.1370.2750.0010.2650.262-0.060-0.0040.6220.6390.3451.0000.0000.0110.0100.0120.290
order_status0.0220.0460.0050.0120.0000.0110.0030.0050.0150.0060.0090.0130.0001.0000.1630.0400.0230.026
review_score0.0800.2050.0200.0140.0310.0120.0150.0110.0110.0190.0140.0140.0110.1631.0000.0100.0480.046
payment_type0.0280.0080.1820.0060.0090.0080.0000.0120.0000.0110.0120.0110.0100.0400.0101.0000.0260.036
customer_state0.0130.0210.0330.0150.0000.0130.0300.0190.0130.0120.0110.0130.0120.0230.0480.0261.0000.030
product_category_name_english0.0340.0200.0900.1010.0270.0920.0540.2120.1500.1920.2600.2670.2900.0260.0460.0360.0301.000

Missing values

2023-02-10T10:56:08.078280image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-10T10:56:08.847401image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-02-10T10:56:09.838618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

customer_idorder_statusorder_purchase_timestamporder_approved_atorder_delivered_carrier_dateorder_delivered_customer_dateorder_estimated_delivery_datereview_scorelength_comment_titlelength_comment_messagereview_answer_timestamppayment_typepayment_installmentspayment_valueproduct_most_frequentnb_itemssum_pricesum_freight_valuecustomer_unique_idcustomer_citycustomer_stateproduct_description_lenghtproduct_photos_qtyproduct_weight_gproduct_length_cmproduct_height_cmproduct_width_cmproduct_category_name_english
09ef432eb6251297304e76186b10a928ddelivered2017-10-02 10:56:332017-10-02 11:07:152017-10-04 19:55:002017-10-10 21:25:132017-10-18 00:00:004.00.0170.02017-10-12 03:43:48credit_card,voucher1.038.7187285b34884572647811a353c7ac498a1.029.998.727c396fd4830fd04220f754e42b4e5bffsao pauloSP268.04.0500.019.08.013.0housewares
1b0830fb4747a6c6d20dea0b8c802d7efdelivered2018-07-24 20:41:372018-07-26 03:24:272018-07-26 14:31:002018-08-07 15:27:452018-08-13 00:00:004.016.020.02018-08-08 18:37:50boleto1.0141.46595fac2a385ac33a80bd5114aec74eb81.0118.7022.76af07308b275d755c9edb36a90c618231barreirasBA178.01.0400.019.013.019.0perfumery
241ce2a54c0b03bf3443c3d931a367089delivered2018-08-08 08:38:492018-08-08 08:55:232018-08-08 13:50:002018-08-17 18:06:292018-09-04 00:00:005.00.00.02018-08-22 19:07:58credit_card3.0179.12aa4383b373c6aca5d8797843e55944151.0159.9019.223a653a41f6f9fc3d2a113cf8398680e8vianopolisGO232.01.0420.024.019.021.0auto
3f88197465ea7920adcdbec7375364d82delivered2017-11-18 19:28:062017-11-18 19:45:592017-11-22 13:39:592017-12-02 00:28:422017-12-15 00:00:005.00.0105.02017-12-05 19:21:58credit_card1.072.20d0b61bfb1de832b15ba9d266ca96e5b01.045.0027.207c142cf63193a1473d2e66489a9ae977sao goncalo do amaranteRN468.03.0450.030.010.020.0pet_shop
48ab97904e6daea8866dbdbc4fb7aad2cdelivered2018-02-13 21:18:392018-02-13 22:20:292018-02-14 19:46:342018-02-16 18:17:022018-02-26 00:00:005.00.00.02018-02-18 13:02:51credit_card1.028.6265266b2da20d04dbe00c5c2d3bb7859e1.019.908.7272632f0f9dd73dfee390c9b22eb56dd6santo andreSP316.04.0250.051.015.015.0stationery
5503740e9ca751ccdda7ba28e9ab8f608delivered2017-07-09 21:57:052017-07-09 22:10:132017-07-11 14:58:042017-07-26 10:57:552017-08-01 00:00:004.00.00.02017-07-27 22:48:30credit_card6.0175.26060cb19345d90064d1015407193c233d1.0147.9027.3680bb27c7c16e8f973207a5086ab329e2congonhinhasPR608.01.07150.065.010.065.0auto
6ed0271e0b7da060a393796590e7b737ainvoiced2017-04-11 12:22:082017-04-13 13:25:17NaNNaN2017-05-09 00:00:002.00.036.02017-05-13 20:25:42credit_card1.065.95a1804276d9941ac0733cfd409f5206eb1.049.9016.0536edbb3fb164b1f16485364b6fb04c73santa rosaRS0.00.0600.035.035.015.0unknown
79bdf08b4b3b52b5526ff42d37d47f222delivered2017-05-16 13:10:302017-05-16 13:22:112017-05-22 10:07:462017-05-26 12:55:512017-06-07 00:00:005.00.00.02017-05-28 02:59:57credit_card3.075.164520766ec412348b8d4caa5e8a18c4641.059.9915.17932afa1e708222e5821dac9cd5db4caenilopolisRJ956.01.050.016.016.017.0auto
8f54a9f0e6b351c431402b8461ea51999delivered2017-01-23 18:29:092017-01-25 02:50:472017-01-26 14:16:312017-02-02 14:08:102017-03-06 00:00:001.00.00.02017-02-05 01:58:35boleto1.035.95ac1789e492dcd698c5c10b97a671243a1.019.9016.0539382392765b6dc74812866ee5ee92a7faxinalzinhoRS432.02.0300.035.035.015.0furniture_decor
931ad1d1b63eb9962463f764d4e6e0c9ddelivered2017-07-29 11:55:022017-07-29 12:05:322017-08-10 19:45:242017-08-16 17:14:302017-08-23 00:00:005.00.00.02017-08-18 01:47:32credit_card,voucher1.0169.769a78fb9862b10749a117f7fc3c31f0511.0149.9919.77299905e3934e9e181bfb2e164dd4b4f8sorocabaSP527.01.09750.042.041.042.0office_furniture
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